Could breathing frequency become a pragmatic means to monitor exercise intensity in atrial fibrillation and coronary heart disease?
Bibliographic record
Abstract
Abstract Introduction Innovations Fund of the Alternate Funding Plan for the Academic Health Sciences Centre of the Ministry of Ontario, Canada; New Investigator Award in Clinical Rehabilitation from the Canadian Institute for Health Research; Heart and Stroke Foundation of Canada Emerging Research Leaders Initiative. Aim Moderate-intensity aerobic exercise, near to the 1st ventilatory threshold (VT1), in adults with atrial fibrillation (AF) and coronary heart disease (CHD) is recommended as a minimum level to derive safe and beneficial physiological gains. The irregular and rapid heart rate (HR) in AF or other heart rhythm disturbances in CHD create challenges for using HR to monitor exercise intensity. The aim of this study was to assess whether breathing frequency (BF) can be used to measure and monitor exercise intensity in people with AF and CHD. Methods An observation study of 30 AF participants with CHD (19M, 11F, 70.7 +/- 8.7 yrs) and 67 non-AF participants with CHD (38M, 29F, 56.9 +/-11.4 yrs) who performed incremental maximal exercise testing with continuous pulmonary gas exchange measures. Results Peak aerobic power (VO2 peak) in AF (17.8 +/- 5.0 ml.kg-1.min-1) was lower than in CHD (26.7 ml.kg-1.min-1) (p <.001). BF peak in AF and CHD were similar (p =.106); 34.6 +/- 5.4 and 36.5 +/- 5.0 breaths.min-1, respectively. In spite of a 14 year age difference, BFs at VT1, were similar in CHD patients with and without AF (23.2 +/- 4.6 and 22.4 +/-4.6 breaths.min-1 respectively). The same was true for BF at %VO2 peak (AF ~59%; CHD ~57%; p = .656). Conclusion In light of newly emerging affordable wearable technologies, this first study of its kind provides an encouraging potential and pragmatic approach for using BF to monitor exercise intensity in AF and CHD.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".