Cardiovascular adaptation to training load in endurance athletes: a longitudinal study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Prior studies on cardiac remodelling associated with exercise have relied on self-reported data of uncertain accuracy. In the present study, exercise duration and intensity were objectively quantified using heart rate (HR) monitors in athletes, and these metrics were correlated with cardiac magnetic resonance findings. METHODS: Young (16-23 years, n = 69) and middle-aged (45-70 years, n = 82) male endurance athletes with ≥80% of training sessions recorded via chest-worn HR monitors over 3 months were included. Training duration, session count, and intensity (classified into five HR zones and expressed as Edwards training impulse in arbitrary units) were analysed. Cardiac magnetic resonance measured indexed left/right ventricular volumes, ejection fraction, and left ventricular mass. RESULTS: Younger athletes trained more than older athletes [169 (127-209) vs 78 (49-114) hours; 23 129 (17 880-28 305) vs 12 620 (7168-17 607) arbitrary units; both P < .05] over a 3-month period. In all athletes, light-to-moderate-intensity training exceeded thresholds of >6 or >9 metabolic equivalent of tasks to describe intense activity. Training duration (r > .33, P < .05 for all) and Edwards training impulse (r > .29, P < .05 for all) correlated with cardiac dimensions, but the duration always outperformed intensity. Time spent in lower HR zones (1 and 2) correlated more with cardiac dimensions than higher-intensity training. Partial least squares analysis identified training duration in Zones 1&2 and 3 and age as key determinants of cardiac remodelling, whereas intensity was not a significant determinant of cardiac dimensions. CONCLUSIONS: Objective exercise quantification reveals new insights into cardiac remodelling, highlighting total exercise duration as a primary determinant of left/right ventricular volumes, independent of intensity. Traditional questionnaire-based methods may overlook these relationships.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".