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Record W4405287372 · doi:10.1016/j.cjca.2024.12.009

Your Heart Can’t See What Sneakers You Are Wearing: Exercise Training Load in Endurance Athletes Is Inadequately Quantified in Sports Cardiology

2024· review· en· W4405287372 on OpenAlexaffvenue
Christophe Dausin, R. Tironi, Véronique Cornelissen, Peter Hespel, Rik Willems, Mark J. Haykowsky, André La Gerche, Guido Claessen, Stephen Foulkes

Bibliographic record

VenueCanadian Journal of Cardiology · 2024
Typereview
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsUniversity of Alberta
FundersKU LeuvenFonds Wetenschappelijk Onderzoek
KeywordsMedicineAthletesCardiologyPhysical therapyEndurance trainingInternal medicineTraining (meteorology)Physical medicine and rehabilitation

Abstract

fetched live from OpenAlex

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 R ESUM E Contexte : La charge d'entranement peut tre un facteur important sous-jacent aux adaptations cardiovasculaires (patho-)physiologiques de l'exercice d'endurance.Pourtant, la quantification de la charge d'entranement demeure complexe en raison de la complexit e de ses multiples composantes (Fr equence, Intensit e, Temps et Type [FITT]).Dans cette revue syst ematique, nous evaluons la manire dont la charge d'entranement a et e quantifi ee dans les etudes de cardiologie sportive et proposons des recommandations sur la manire d'am eliorer cette quantification.M ethodes : Une recherche exhaustive a et e effectu ee dans PubMed et EMBASE jusqu'en octobre 2024.Les etudes portant sur la cardiologie sportive, la charge d'entranement et les sports d'endurance ont et e incluses.L'extraction des donn ees a port e sur les caract eristiques des etudes, les m ethodes d' evaluation de la charge d'entranement, les cons equences cardiovasculaires et les profils des athltes.R esultats : Au total, 62 etudes avec 1 060 700 participants ont et e incluses dans notre analyse.La majorit e des etudes (59,7 %) portait sur le remodelage cardiaque induit par l'exercice, les autres sujets etant les arythmies cardiaques (12,9 %), l'adaptation cardiaqueRegular endurance exercise is a potent stimulus for structural, electrical, and functional cardiovascular adaptations.These physiologic adaptations-broadly termed exercise-induced cardiac remodelling (EICR)-are necessary to allow the cardiovascular system to meet the substantial hemodynamic and metabolic demands of endurance exercise and competition. 1,2

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0110.004
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.319
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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