Non-linear is not superior to linear aerobic training periodization in coronary heart disease patients
Bibliographic record
Abstract
Background We aimed to compare: (1) two different periodized aerobic training protocols (linear (LP) versus non-linear (NLP)) on the cardiopulmonary exercise response in patients with coronary heart disease; (2) the proportion of responders between both training protocols. Design A randomized controlled trial. Methods A total of 39 coronary heart disease patients completed either LP ( n = 20, 65 ± 10 years) or NLP ( n = 19, 66 ± 5 years). All patients completed a cardiopulmonary exercise testing with gas exchange measurements. Patients underwent a 12-week supervised exercise program including an isoenergetic aerobic periodized training and a similar resistance training program, 3 times/week. Weekly energy expenditure was constantly increased in the LP group for the aerobic training, while it was deeply increased and intercepted with a recovery week each fourth week in the NLP group. Peak oxygen uptake (peak V̇O 2 ), oxygen uptake efficiency slope, ventilatory efficiency slope (V̇E/V̇CO 2 slope), V̇O 2 at the first (VT 1 ) and second (VT 2 ) ventilatory thresholds, and oxygen pulse (O 2 pulse) were measured. Responders were determined according the median value of the Δpeak V̇O 2 (mL.min −1 .kg −1 ). Results We found similar improvement for peak V̇O 2 (LP: +8.1%, NLP: +5.3%, interaction: p = 0.37; time: p < 0.001) and for oxygen uptake efficiency slope, VT 1 , VT 2 and O 2 pulse in both groups (interaction: p > 0.05; time: p < 0.05) with a greater effect size in the LP group. The proportion of non-, low and high responders was similar between groups ( p = 0.29). Conclusion In contrast to the athletes, more variation (NLP) does not seem necessary for greater cardiopulmonary adaptations in coronary heart disease patients.
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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".