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Record W4220822272 · doi:10.1080/24733938.2022.2060520

Assessing the usefulness of submaximal exercise heart rates for monitoring cardiorespiratory fitness changes in elite youth soccer players

2022· article· en· W4220822272 on OpenAlexaff
Stefan Altmann, Ludwig Ruf, Rainer Neumann, Sascha Härtel, Alexander Wöll, Martin Buchheit

Bibliographic record

VenueScience and Medicine in Football · 2022
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsInterior Health
Fundersnot available
KeywordsCardiorespiratory fitnessHeart rateTest (biology)TreadmillBlood lactatePhysical therapyMedicineInternal medicineBiologyBlood pressure

Abstract

fetched live from OpenAlex

AIM: This study aimed to assess the value of monitoring fitness in elite youth soccer players (U15 to U19 age groups) by analyzing the concomitant changes in heart rate at submaximal intensity (HR12km/h) and the velocity at a lactate concentration of 4 mmol/l (v4mmol/l). METHODS: Players were tested by means of an incremental treadmill test on two occasions during the summer pre-season in two consecutive seasons. Based on data from a total of 170 test comparisons from the U15 (n = 48 test comparisons), U16 (n = 40 test comparisons), U17 (n = 46 test comparisons), and U19 (n = 36 test comparisons) age groups, the agreement between substantial changes in HR12km/h and v4mmol/l was analyzed using the threshold combination of HR12km/h = 4.5% and v4mmol/l = 6.0%. RESULTS: Results revealed 2% full mismatches, 36% partial agreements, and 62% full agreements for the whole sample in terms of fitness change interpretation between both variables. The respective values for the U15 to U19 age groups ranged between 0% and 5% full mismatches, 28-44% partial agreements, and 56-68% full agreements with no meaningful differences between age groups. CONCLUSION: In conclusion, our findings confirm the practical value of using HR12km/h to monitor fitness changes in elite youth soccer players when lactate sampling during incremental tests is not possible.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.097
GPT teacher head0.372
Teacher spread0.275 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2022
Admission routes1
Has abstractyes

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