Variability of cardiopulmonary exercise testing in patients with atrial fibrillation and determination of exercise responders to high-intensity interval training and moderate-to-vigorous intensity continuous training
Bibliographic record
Abstract
Disabling atrial fibrillation (AF)-related symptoms and different testing settings may influence day-to-day cardiopulmonary exercise testing (CPET) measurements, which can affect exercise prescription for high-intensity interval training (HIIT) and moderate-to-vigorous intensity continuous training (M-VICT) and their outcomes. This study examined the reliability of CPET in patients with AF and assessed the proportion of participants achieving minimal detectable changes (MDC) in peak oxygen consumption (V̇O 2peak ) following HIIT and M-VICT. Participants were randomized into HIIT or M-VICT after completing two baseline CPETs: one with cardiac stress technologists (CPET diag ) and the other with a research team of exercise specialists (CPET research ). Additional CPET was completed following 12 weeks of twice-weekly training. The reliability of CPET diag and CPET research was assessed by intraclass correlation coefficient (ICC) and dependent t tests. The MDC score was calculated for V̇O 2peak using a reliable change index. The proportion of participants achieving MDC was compared between HIIT and M-VICT using chi-square analysis. Eighteen participants (69 ± 7 years, 33% females) completed two baseline CPETs. The ICCs were significant for all measured variables. However, peak power output (PO peak : 124 ± 40 vs. 148 ± 40 watts, p < 0.001) and HR (HR peak : 136 ± 22 vs. 148 ± 30 bpm, p = 0.023) were significantly greater in CPET research than CPET diag . Few participants achieved MDC in V̇O 2peak (5.6 mL/kg/min) with no difference between HIIT (0%) and M-VICT (10.0%, p = 0.244). PO peak and HR peak differed significantly in patients with AF when CPETs were repeated under different settings. Caution must be practised when prescribing exercise intensity based on these measures as under-prescription may increase the number of exercise non-responders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".