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Record W2790057164 · doi:10.1371/journal.pone.0192231

Effect of repeated-sprints on the reliability of short-term parasympathetic reactivation

2018· article· en· W2790057164 on OpenAlexaff
Matteo Bonato, Andrea Meloni, Giampiero Merati, Antonio La Torre, Luca Agnello, Gianluca Vernillo

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Calgary
FundersUniversità degli Studi di Milano
KeywordsIntraclass correlationHeart rate variabilityCoefficient of variationReliability (semiconductor)MathematicsMedicineCardiologyStatisticsHeart rateReproducibilityInternal medicineBlood pressurePhysics

Abstract

fetched live from OpenAlex

This study determined the reliability of post-exercise heart rate recovery (HRR) and vagal-related HR variability (HRV) after repeated-sprints (RSs), and contrasted it with the smallest worthwhile change (SWC) of these indices. Fourteen healthy male participants performed on four occasions, separated by 7 days, five 30-m sprints interspersed by 25-s of recovery. Post-exercise HR during 10 min of seated rest was measured. HRR during the first 60-s of recovery was computed (HRR60s). HRV indices were calculated in time and frequency domains during the last 5-min of the recovery. Absolute and relative reliability were assessed by typical error of measurement expressed as coefficient of variation (CV) and intraclass correlation coefficients (ICCs), respectively. Sensitivity was assessed comparing SWC to the typical error of measurement. CV ranged from 3.6% to 13.5% and from 6.3% to 109.2% for the HRR and HRV indices, respectively. ICCs were from 0.78 to 0.96 and from 0.76 to 0.92, respectively. HRR and HRV indices showed large discrepancies reliability. HRR60s and the square root of the mean sum of the squared differences between R-R intervals presented the highest levels of both absolute and relative reliability. However, SWC was lower than the typical error of measurement, indicating insufficient sensitivity to confidently detect small, but meaningful, changes in HRR and HRV indices.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.034
GPT teacher head0.268
Teacher spread0.234 · 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 designBench or experimental
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

Citations7
Published2018
Admission routes1
Has abstractyes

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