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Record W2320066359 · doi:10.1080/02640414.2016.1166391

Heart rate recovery after aerobic and anaerobic tests: is there an influence of anaerobic speed reserve?

2016· article· en· W2320066359 on OpenAlexaboutno aff
Sebastián Del Rosso, Fábio Yuzo Nakamura, Daniel Boullosa

Bibliographic record

VenueJournal of Sports Sciences · 2016
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
Fundersnot available
KeywordsAnaerobic exerciseSprintHeart rateAerobic exerciseMedicinePhysical therapyAudiologyInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

The present study assessed if differences in the metabolic profile, inferred from the anaerobic speed reserve (ASR), would influence the dynamics of heart rate recovery (HRR) after two modes of exercise. Thirty-nine physical education students (14 females and 25 males) volunteered for this study. Participants carried out three separate testing sessions to assess maximal sprinting speed (MSS, 1st session), repeated sprint ability (RSA, 2nd session) and maximal aerobic speed (MAS) using the Université of Montreal Track Test (UMTT, 3rd session). ASR was defined as the difference between MSS and MAS. Heart rate was continuously registered throughout the tests and during the 5-min post-test recovery. To evaluate the influence of ASR on post-exercise, HRR comparisons between ASR-based groups [high ASR vs. low ASR] and sex groups (males vs. females) were performed. Significant differences (P < 0.05) were found between high ASR and low ASR groups of the same sex for indices of relative HRR after the RSA and UMTT. In addition, after the RSA test, males from the high ASR group had a significantly slower HRR kinetics compared with the males of the low ASR (P < 0.05) and the females of high ASR (P < 0.05); whereas females of the high ASR groups had a faster HRR kinetics compared with the females of low ASR group (P < 0.05). Our results showed that in males, post-exercise HRR could be related to the ASR, whereas in females, the influence of ASR is less clear.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.274
Teacher spread0.257 · 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

Citations31
Published2016
Admission routes1
Has abstractyes

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