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 V̇O2), oxygen uptake efficiency slope, ventilatory efficiency slope (V̇E/V̇CO2 slope), V̇O2 at the first (VT1) and second (VT2) ventilatory thresholds, and oxygen pulse (O2 pulse) were measured. Responders were determined according the median value of the Δpeak V̇O2 (mL.min−1.kg−1). Results We found similar improvement for peak V̇O2 (LP: +8.1%, NLP: +5.3%, interaction: p = 0.37; time: p < 0.001) and for oxygen uptake efficiency slope, VT1, VT2 and O2 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 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".