Your Heart Can’t See What Sneakers You Are Wearing: Exercise Training Load in Endurance Athletes Is Inadequately Quantified in Sports Cardiology
Bibliographic record
Abstract
BACKGROUND: Training load may be an important factor underlying the (patho-)physiologic cardiovascular adaptations from endurance exercise. Yet, quantifying training load remains challenging due to the complexity of its components (Frequency, Intensity, Time, and Type [FITT]). In this systematic review we evaluate how training load has been quantified in sports cardiology studies and provide recommendations for how this can be improved. METHODS: A comprehensive search was conducted across PubMed and EMBASE up to October 2024. Studies involving "sports cardiology," "training load," and "endurance sport" were included. Data extraction included study characteristics, training load assessment methods, cardiovascular outcomes, and athlete profiles. RESULTS: A total of 62 studies with 1,060,700 participants were included in our review. The majority of studies (59.7%) focused on exercise-induced cardiac remodelling, with other topics being cardiac arrhythmias (12.9%), cardiac autonomic adaptation (3.2%), exercise dose-response (6.5%), and coronary heart disease (17.7%). Training load was primarily quantified by questionnaires (58.1%), whereas heart rate monitoring, a more objective measure, was used in only 1.6% of the studies. All studies reported exercise type, but only 19.4% measured all FITT components. CONCLUSIONS: There is a lack of uniformity in the assessment of key FITT variables to quantify training load within the field of sports cardiology, with many studies relying on subjective or incomplete methods. As cardiology moves into the precision medicine era, researchers and clinicians should seek to obtain objective training load information from their athletes according to the FITT framework, and data from use of objective wearable devices represent the optimal way to do this.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".